Atrial Fibrosis and Inflammation in Postoperative Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EVs) isolated from human heart-derived cells have shown promise in suppressing inflammation and fibroblast proliferation. However, their precise benefits in atrial fibrillation (AF) prevention and the role of their antifibrotic/anti-inflammatory properties remain unclear. OBJECTIVES: The purpose of this study was to conduct a head-to-head comparison of antiarrhythmic strategies to prevent postoperative AF using a rat model of sterile pericarditis. Specifically, we aimed to assess the efficacy of amiodarone (a classic antiarrhythmic drug), colchicine (an anti-inflammatory agent), and EVs derived from human heart-derived cells, which possess anti-inflammatory and antifibrotic properties, on AF induction, inflammation, and fibrosis progression. METHODS: Heart-derived cells were cultured from human atrial appendages under serum-free xenogen-free conditions. Middle-aged Sprague Dawley rats were randomized into different groups, including sham operation, sterile pericarditis with amiodarone treatment, sterile pericarditis with colchicine treatment (2 dose levels), and sterile pericarditis with intra-atrial injection of EVs or vehicle. Invasive electrophysiological testing was performed 3 days after surgery before sacrifice. RESULTS: Sterile pericarditis increased the likelihood of inducing AF. Colchicine and EVs exhibited anti-inflammatory effects, but only EV treatment significantly reduced AF probability, whereas colchicine showed a positive trend without statistical significance. EVs and high-dose colchicine reduced atrial fibrosis by 46% ± 2% and 26% ± 2%, respectively. Amiodarone prevented AF induction but had no effect on inflammation or fibrosis. CONCLUSIONS: In this study, both amiodarone and EVs prevented AF, whereas treatment with colchicine was ineffective. The additional anti-inflammatory and antifibrotic effects of EVs suggest their potential as a comprehensive therapeutic approach for AF prevention, surpassing the effects of amiodarone or colchicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".